Alboaneen, Dabiah Ahmed

Tianfield, Huaglory

Zhang, Yan

English

Training multi-layer perceptron (MLP) is non-trivial due to its non-linear nature and the presence of large number of local optima. Meta-heuristic algorithms may solve this problem efficiently. In this paper, we investigate the use of glowworm swarm optimisation (GSO) algorithm in training the MLP neural network. The GSO based trainer is evaluated on five classification datasets, namely Wisconsin breast cancer, BUPA liver disorders, vertebral column, exclusive OR (XOR) and balloons. The evaluations are conducted by comparing the proposed trainer with four other meta-heuristics, namely biogeography-based optimisation (BBO), genetic algorithm (GA), bat (BAT) and multi-verse optimiser (MVO) algorithms. The results show that our proposed trainer achieves better classification accuracy rate in most datasets compared to the other algorithms

ResearchOnline@GCU

Glowworm swarm optimisation for training multi-layer perceptrons

https://researchonline.gcu.ac.uk/files/25435300/mlp.pdf

Glowworm swarm optimisation for training multi-layer perceptrons

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ResearchOnline@GCU